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Many studies have investigated the fake review detection problem and its challenges. The main task associated with fake review detection is classifying the review as fake or genuine. In this survey paper, we have presented a comprehensive survey of the literature to further identify existing problems for future directions in this research area.
Motivated by previous work, Nilizadeh et al. [142] proposed OneReview fake review detection model based on textual and metadata features. OneReview concentrates on separating anomalous changes in business profiles via multiple review websites to discover harmful activity without depending on particular patterns. OneReview used change point analysis techniques on each review from numerous websites. Then, they evaluated the change point to identify any reviews which did not match through the webpages. After classifying them as suspicious with the introduced change point analyser, they used the Random Forest classifier to identify the fake reviews. They evaluated the proposed method on two datasets; Yelp Data Challenge dataset (https://www.yelp.com/dataset) and dataset crawled from TripAdvisor. The experimental results showed that the proposed method performed well in fake reviews detection, with an accuracy of 97% with combining all features and 86% with textual features. However, the change point analyser evaluated time series data in one month; this may create some latency issues between the OneReview classification and the posted review.
More recently, Zeng et al. [50] introduced an ensemble model to detect fake reviews dependent on the review structure. They found the followings; fake reviews expressed stronger emotions than genuine reviews. The first and last sentences contained stronger emotions than the middle sentence, and fake reviews start or end with similar sentences. The proposed model consists of four separate bidirectional LSTM to encode the middle, beginning, and end of the review. Then the concatenation of four representations is used to detect fake reviews. The self-attention method was utilized to integrate the three-local representation into one representation. In contrast, the attention method was applied to incorporate the two representations into the final representation. The results on the AMT dataset [100] showed that the proposed model achieved better results than other methods such as SWNN and SAGA in one domain (Hotel, Doctor, and restaurant) with an 85.7%, 84.7%, and 85.5% accuracy, respectively. Furthermore, in the mixed domain. It achieved 83.4% accuracy. However, the proposed model failed to achieve good results in cross-domain with 71.6% on the restaurant domain and 60.5% on the doctor domain.
SUMMARY: Neural networks are one of the most effective machine learning methods. For this area of research, deep learning is used and achieved significant outcomes. Further, there is no need for feature extraction for deep learning; these can be extracted directly from the input dataset without any learned knowledge or interventions. However, these models also have some limitations when applied in fake review detection. One of the main problems associated with deep learning algorithms is that deep learning models don't provide a comprehensive understanding of learning. Deep learning can be considered a "black box" model that does not have declarative knowledge to explain the outcomes. Moreover, it requires a large amount of data compared with traditional machine learning, which means we cannot use deep learning algorithms with a small dataset. Furthermore, deep learning models require extremely computational resources.
In this section, a first-hand evaluation of the performances of seven promising deep learning algorithms on two datasets is presented. These algorithms are character-level convolutional -LSTM, convolutional -LSTM, HAN, convolutional HAN, BERT, DistilBERT, and RoBERTa. The main goal is investigating to what extent such algorithms are able to detect fake reviews. Note that, some of these algorithms have been used by researchers in different domains [174]–[179]. However, as of yet, they have not been used in the fake review detection field. Therefore, this study demonstrated the efficiency of such algorithms in detecting fake review, which can pose as a baseline for further research. For the initial experiments in this study, we used two datasets. The first dataset is the "Yelp Consumer Electronic dataset" [79] that crawled through review datasets based on the web-scraper process from Yelp.com. They labelled them based on content and user behavioural features. This dataset was annotated based on the rule-based method. For example, the dataset was constructed on some rules that considered the review as a fake if different/ same users posted reviews of the different/ same product. This dataset presents a real-life dataset which is preferred as this will help the researchers to build a fake review detection model that can be used efficiently in the real world. A second dataset is the "deception dataset" [100] constructed from TripAdvisor and Amazon Mechanical Turk websites from Chicago city, which contains 3,032 reviews from different domains (Hotel, Restaurant, and Doctor) by crowdsourcing platform. This dataset has extensively used in literature, and it is semi-real dataset [3], [4], [12], [27], [29], [32], [37], [65]. For simplicity, we combined these three-domain reviews at current stages, and we leave the investigation of each domain separately (i.e., multi-domain detection model) for future work. As can be seen from the previous section, to design a fake review detection model, the following steps are performed.
The C-LSTM extracts a sequence of higher-level phrase representations using CNN and feeds the sequence into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation [174]. For each word in a given sentence, the convolutional layer applies a matrix-vector function. LSTM propagates historical information over the neural network chain. In our work, first, CNN is built to learn the higher representation of n-grams on top of the pre-trained word vector. Then, the feature maps CNN that are designed as sequential window features to serve as the input of LSTM in order to learn sequential correlations from high sequence representations. This turns each sentence into a succession window (n-gram) features to activate factors in sentences. In our work, we used one LSTM layer and one convolutional layer with 128 filters. Then, we fed it into LSTM architecture with dropout 0.2 and 100 output dimensions. Finally, we used the sigmoid function for the output layer to classify the review as fake or genuine.
Cross domain fake review detection. The cross-domain problem needs to be effectively addressed. The issue of lack of annotation datasets is disappointing in fake review detection. Applying a model trained in the source domain and tested in the target domains is a significant research direction. The existing literature focused only on one domain of fake review detection, so these proposed models failed when training in the domain and tested in other domains. For example, the authors [80] trained the model in the domain and tested the other domain. The experimental results show the performance has significantly dropped compared with the performance in the same domain. More research and investigation are needed for cross-domain fake reviews detection [67], [186].
We can conclude that most of the existing works focused on supervised machine learning to detect fake reviews. However, supervised machine learning needs a labelled dataset to predict whether the review is fake or not, which can be hard to obtain in a fake review detection area. According to the difficulty of obtaining labelled dataset, we observed that the most commonly used datasets in the current works are constructed based on a crowdsourcing framework. Evaluating the machine learning techniques on these datasets is not preferred as these datasets do not present the fake review in a real-world application. Consequently, assessing the classifiers on the real-world application is preferred as this will help us developing algorithms that can work efficiently in the real world.
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This most basic bets allows players to wager on one side or the other, and usually pays $10 for every $11 wagered (-110).
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Illinois waived the in-house registration requirement for much of 2020 due to the coronavirus pandemic, but recently switched from online registration back to the much-maligned in-house registration requirement.
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parent county
knox and grainger rutherford county humphreys county roane & indian lands knox county indian lands anderson & claiborne rutherford, smith & warren indian lands washington county davidson, dickson, montgomery, & robertson henderson, mcnairy & madison grainger & hawkins jackson & overton jefferson county bedford, warren & franklin haywood, madison, dyer & gibson white, bledsoe, rhea, morgan, fentress & putnam part of north carolina perry county franklin, cannon, jackson & white montgomery and robertson indian lands indian lands morgan, overton & white rutherford & indian lands indian lands indian lands hawkins & knox counties washington county coffee, warren & franklin jefferson, grainger & greene rhea & indian lands hawkins & claiborne hardin & indian lands indian lands sullivan county indian lands indian lands indian lands dickson county dickson, humphreys, montgomery & stewart stewart county smith county & indian lands greene & hawkins carter county greene & hawkins obion county haywood, dyer & tipton hickman & indian lands hickman, lawrence, maury & wayne bedford county roane, monroe, blount & mcminn smith and sumner counties indian lands indian lands giles, bedford, lincoln & maury williamson & indian lands indian lands hardin county rhea county indian lands tennessee county bedford, lincoln & franklin anderson & roane indian lands jackson & indian lands humphreys & hickman fentress & overton mcminn & bradley fentress, jackson, smith, white & overton roane county knox & indian lands tennessee & sumner davidson, williamson & wilson anderson, campbell, fentress & morgan counties hamilton, marion & warren jefferson county chickasaw nation lands sumner & indian lands montgomery county washington county davidson county shelby county wilson, macon, smith & sumner counties washington & carter grainger, claiborne, campbell, anderson & knox warren & white white, jackson, smith & indian lands part of north carolina hickman county indian lands jackson & smith davidson county sumner county |
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I won the first few games in a row with a lot of heart.
A wine and ice machine with built-in speakers, so you can enjoy a glass of red with your friends without having to leave your bed.
It was easy to use and I was able to make so many different tastes with one machine.
A three-pack of reusable, reusable food containers that'll make your meals look delicious.
[Image] These containers are designed to be leak-proof and leak-proof up to 100 times.
L 28.
A set of four magnetic spice jars that'll keep your spices in place and look organized.
A microwave pasta cooker
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